Geometrical Input Parameters Research Articles

Customizable metamaterial design for desired strain-dependent Poisson’s ratio using constrained generative inverse design network

Inverse design of metamaterial structures with customized strain-dependent Poisson’s ratio has significant potential across various applications. However, achieving precise control over these mechanical properties presents a challenge due to the complex relationship between geometry and mechanical performance. Here, we present a novel data-driven approach utilizing a constrained generative inverse design network (CGIDN) to address this challenge. The CGIDN uses backpropagation to efficiently navigate the design space and achieve target mechanical properties with high accuracy. Our method starts by generating a comprehensive dataset of Poisson’s ratio-strain curves for various geometries incorporating cuts. These curves are then compressed using principal component analysis (PCA) to reduce dimensionality while preserving essential features. A deep neural network (DNN) is then trained to map input geometric parameters to these principal components, with the architecture optimized using grid search. The CGIDN facilitates the inverse design process by recommending geometric parameters for unit cell designs that match specified target Poisson’s ratio-strain curves. We validated the effectiveness of our approach through Finite Element Analysis (FEA) and experimental verification. The FEA results for the designed unit cells showed high agreement with the target and predicted curves, demonstrating the accuracy of the CGIDN model. Further, tensile tests on specimens confirmed that the inverse-designed structures reproduced the desired mechanical behavior upon scale-up. Our method, which enables efficient and accurate design of metamaterials with tailored mechanical properties, holds promise for applications in wearable devices, soft robotics, and advanced sensor systems

Prediction of elastic properties of woven polymer composites using micromechanical models and measured microstructural parameters

Prediction of elastic properties of plain weave composites using two well-known micromechanical models is addressed, along with a dedicated finite element model of the unit cell which includes details of the woven architecture. Predictions of micromechanical models are carried out using geometric input parameters statistically measured from micrographies of the unit cell of the plain weave composite and compared to finite element predictions and to measured elastic properties of an E-glass/vinyl ester plain weave composite. The micromechanical models predict that the width and thickness of the yarn in the fill and warp directions greatly influence the elastic properties and anisotropy of the plain weave composite. Good agreement between all approaches and the measured values is observed for the in-plane elastic properties, as long as the input geometric parameters of the unit cell required for the models are measured with statistical rigor. However, transverse shear moduli are not accurately predicted by the examined micromechanical models, only by the finite element method. Reasons for such discrepancies are discussed and supported by modeling findings and digital image correlation full field strain maps.

Data driven models for capacity prediction of CFS lipped channel flexural members

Cold-Formed Steel (CFS) is considered as a sustainable material and CFS members are becoming popular nowadays as primary or secondary load carrying structural elements. The increasing use of CFS members is owing to their other advantages like high strength to weight ratio, versatility in cross sectional shapes and dimensions, easy fabrication and mass production, uniform quality, good energy efficiency, easiness to handle and transport etc. The behaviour of cold-formed members under flexure is complex as it involves the occurrence of various buckling modes (Local, distortional or lateral-torsional buckling) and the possible interaction between them. Although, the moment carrying capacity of CFS beams can be estimated using existing codes and design standards, the present study is an attempt to introduce an alternate rapid capacity assessment tool based on machine learning for the same. Available data on experiments conducted on CFS lipped channel beams are collected to model and analyse in ABAQUS for validating the finite element model. The validated FE model is used for developing the input-output data set for training the Machine Learning (ML) models by sampling the geometric and strength related input parameters of the CFS beam. Recently introduced machine learning regression models like Linear Regression, Lasso regression, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Gradient Boosting Regressor, Support Vector Machine and Artificial Neural Network are used to predict the ultimate flexural capacity of CFS beams. The CatBoost model is emerged as the best performing algorithm for predicting the flexural capacity, with 98.5% accuracy for the test data set. A SHAP (SHapley Additive exPlanations) analysis is also conducted for the global and local interpretation of the prediction of the best performing machine learning model. SHAP results not only indicate the influence of input parameters on output prediction, but also the influence of input parameters on each other in predicting the output.

Recommendations for finite element modelling of nickel-titanium stents-Verification and validation activities.

The objective of this study is to present a credibility assessment of finite element modelling of self-expanding nickel-titanium (Ni-Ti) stents through verification and validation (VV) activities, as set out in the ASME VV-40 standard. As part of the study, the role of calculation verification, model input sensitivity, and model validation is examined across three different application contexts (radial compression, stent deployment in a vessel, fatigue estimation). A commercially available self-expanding Ni-Ti stent was modelled, and calculation verification activities addressed the effects of mesh density, element integration and stable time increment on different quantities of interests, for each context of use considered. Sensitivity analysis of the geometrical and material input parameters and validation of deployment configuration with in vitro comparators were investigated. Results showed similar trends for global and local outputs across the contexts of use in response to the selection of discretization parameters, although with varying sensitivities. Mesh discretisation showed substantial variability for less than 4 × 4 element density across the strut cross-section in radial compression and deployment cases, while a finer grid was deemed necessary in fatigue estimation for reliable predictions of strain/stress. Element formulation also led to substantial variation depending on the chosen integration options. Furthermore, for explicit analyses, model results were highly sensitive to the chosen target time increment (e.g., mass scaling parameters), irrespective of whether quasistatic conditions were ensured (ratios of kinetic and internal energies below 5%). The higher variability was found for fatigue life simulation, with the estimation of fatigue safety factor varying up to an order of magnitude depending on the selection of discretization parameters. Model input sensitivity analysis highlighted that the predictions of outputs such as radial force and stresses showed relatively low sensitivity to Ni-Ti material parameters, which suggests that the calibration approaches used in the literature to date appear reasonable, but a higher sensitivity to stent geometry, namely strut thickness and width, was found. In contrast, the prediction of vessel diameter following deployment was least sensitive to numerical parameters, and its validation with in vitro comparators offered a simple and accurate (error ~ 1-2%) method when predicting diameter gain, and lumen area, provided that the material of the vessel is appropriately characterized and modelled.

Novel CFD-DEM approach for analyzing spherical and non-spherical shape particles in spouted fluidized bed

The spouted fluidized bed process is a multiphase heat and mass transfer flow. The process requires effective solid-gas and solid-solid interaction. Various numerical models have been developed to understand and optimize these interactions. However, most of these models have used spherical particle definition, whereas the actual particles are non-spherical. Here we present coupled Computational Fluid Dynamics(CFD) and Discrete Element Method(DEM) based model to analyze the fluidized bed process for non-spherical particles (Faceted cylinder). The model results are validated with experimental results for spherical particles. The model is further used to understand mixing during the process as a function of non-spherical particle geometry. Aspect ratio and corner count were the geometrical input parameters for faceted cylinder-shaped particles. Transient plots for bubble diameter and bed height for spouted fluidized beds were created for all cases. The bubble diameter and bed height values were much lower for faceted cylinder particles than for spherical particles. For non-spherical particles, the increased number of corners was a crucial factor that brought the outcomes closer to those of spherical-shaped particles. The aspect ratio values increased, and the bubble diameter shrank as a result of more resistance to particle movement. The results would be of great use to correctly simulate non- spherical particles based fluidized bed process and optimize various process parameters.

Determining the influence and correlation for parameters of flexible forming using the random forest method

Single-point incremental forming (SPIF) enables the forming of fix-clamped sheet metal by moving a relatively small geometrically simple tool along the trajectory, producing the desired shape of the final product. Excessive thinning of the sheet results in fracture, determining the limit of formability. This characteristic of the forming process can be improved by upgrading the basic SPIF process to two-step forming, whereby a more even distribution of the sheet thickness can be achieved by pre-bulging with a hemispherical punch. This study focused on analysing the SPIF process and a hybrid two-step forming consisting of sequential bulging and SPIF. The analysis focused on the output parameters of sheet metal thinning and maximum forming force components and was conducted with Abaqus simulation software. An innovative new approach for influence analysis of technological, material and geometrical input parameters and correlation analysis between the mentioned parameters was performed using the random forest (RF) method, which allows the determination of individual parameter influence by analysing tree-shaped models obtained through the training process. The analysis results show a significant influence of the workpiece wall angle and part depth on thinning and initial sheet thickness on values of the forming force components. The results also show a great correlation between the parameters of the bulging depth and the part depth after SPIF and the significant influence of the appropriate choice of the backing plate geometry for the target product geometry.

A CFD-based approach on thermohydraulic performance evaluation and optimisation of the wavy corrugated curved channel solar air heater

ABSTRACT The present paper addresses the optimisation of geometrical design and flow parameters of the curved channel solar air heater (SAH). The prominence on heat transfer enhancement, absorber plate temperature, turbulence and thermo-hydraulic performance factors by changing the design and geometrical input parameters such as height, length and width has been thoroughly discussed. In addition, the consequences of amplitude and wavelength of wavy corrugated curved passage SAH are also studied. Here, the effects of different flow and geometrical parameters viz., Reynolds number ( 2000 ≤ R e ≤ 24000 ) , length ( 1 ≤ L ≤ 5 m ) , width ( 0.1 ≤ W ≤ 1 m ) , height ( 0.010 ≤ h ≤ 0.05 m ) , wavelength ( 0.025 ≤ λ ≤ 0.125 m ) and amplitude ( 0.004 ≤ α ≤ 0.024 m ) are investigated and presented at the optimum values on the basis of outlet air temperature and thermo-hydraulic efficiency. Finally, a wavy corrugated surface in the curved channel SAH can expand the heat transfer coefficient to a greater extent in comparison to the smooth wavy curved channel SAH.

Multi-Attribute Machine Learning Model for Electrical Motors Performance Prediction

Designing an electrical motor is a complex process that needs to deal with the non-linearity phenomena caused by the saturation of the iron at high magnetic field strength, the multi-physical nature of the investigated system and with requirements that may come into conflict. This paper proposes to use geometric parametric models to evaluate the multi-physical performances of electrical machines and build a machine learning model that is able to predict multi-physical characteristics of electrical machines from input geometrical parameters. The focus of this work is to accurately estimate the electromagnetic characteristics, motor losses and stator natural frequencies, using the developed machine learning model, at the early-design stage of the electrical motor, when the information about the housing is not available and to include the model in optimisation loops, to speed-up the computational time. Three individual machine learning models are built for each physics analysed, a model for the torque and back electromotive force harmonic orders, one model for motor losses and another one for natural frequencies of the mode-shapes. The necessary data is obtained by varying the geometrical parameters of 2D electromagnetic and 3D structural motor parametric models. The accuracy of different machine learning regression algorithms are compared to obtain the best model for each physics involved. Ultimately, the developed multi-attribute model is integrated in an optimisation routine, to compare the computational time with the classical finite element analysis (FEA) optimisation approach.

Machine-learning-based prediction of the effectiveness of the delivered dose by exhale-gated radiotherapy for locally advanced lung cancer: The additional value of geometric over dosimetric parameters alone.

This study aimed to assess interfraction stability of the delivered dose distribution by exhale-gated volumetric modulated arc therapy (VMAT) or intensity-modulated arc therapy (IMAT) for lung cancer and to determine dominant prognostic dosimetric and geometric factors. Clinical target volume (CTVPlan) from the planning CT was deformed to the exhale-gated daily CBCT scans to determine CTVi, treated by the respective dose fraction. The equivalent uniform dose of the CTVi was determined by the power law (gEUDi) and cell survival model (EUDiSF) as effectiveness measure for the delivered dose distribution. The following prognostic factors were analyzed: (I) minimum dose within the CTVi (Dmin_i), (II) Hausdorff distance (HDDi) between CTVi and CTVPlan, (III) doses and deformations at the point in CTVPlan at which the global minimum dose over all fractions per patient occurs (PDmin_global_i), and (IV) deformations at the point over all CTVi margins per patient with the largest Hausdorff distance (HDPworst). Prognostic value and generalizability of the prognostic factors were examined using cross-validated random forest or multilayer perceptron neural network (MLP) classifiers. Dose accumulation was performed using back deformation of the dose distribution from CTVi to CTVPlan. Altogether, 218 dose fractions (10 patients) were evaluated. There was a significant interpatient heterogeneity between the distributions of the normalized gEUDi values (p<0.0001, Kruskal-Wallis tests). Accumulated gEUD over all fractions per patient was 1.004-1.023 times of the prescribed dose. Accumulation led to tolerance of ~20% of fractions with gEUDi <93% of the prescribed dose. Normalized Dmin >60% was associated with predicted gEUD values above 95%. Dmin had the highest importance for predicting the gEUD over all analyzed prognostic parameters by out-of-bag loss reduction using the random forest procedure. Cross-validated random forest classifier based on Dmin as the sole input had the largest Pearson correlation coefficient (R=0.897) in comparison to classifiers using additional input variables. The neural network performed better than the random forest classifier, and the gEUD values predicted by the MLP classifier with Dmin as the sole input were correlated with the gEUD values characterized by R=0.933 (95% CI, 0.913-0.948). The performance of the full MLP model with all geometric input parameters was slightly better (R=0.952) than that based on Dmin (p=0.0034, Z-test). Accumulated dose distributions over the treatment series were robust against interfraction CTV deformations using exhale gating and online image guidance. Dmin was the most important parameter for gEUD prediction for a single fraction. All other parameters did not lead to a markedly improved generalizable prediction. Dosimetric information, especially location and value of Dmin within the CTV i , are vital information for image-guided radiation treatment.

Experimental and numerical study on flexural behaviour of cold-formed steel hat-shaped beams with geometrical imperfections

Experimental and numerical studies are conducted to investigate cold-formed hat-shaped members' flexural behaviour and determine the effects of different parameters on the behaviour. A total of ten specimens of cold-formed hat-shaped beams with different lengths are chosen for physical tests. The actual geometry and geometric imperfection distributions are acquired by processing 3D point clouds obtained by laser scanners. Four-point bending tests are performed to obtain benchmark results for numerical models. It is observed that numerical results are in accordance with the experimental results with high accuracy. Once the generated finite element models are calibrated based on the experiment results, a parametric study investigating the influence of the length, thickness, lip width, return length, radius, and lip angle on the beam behaviour is undertaken. In addition, the effect of geometric imperfection magnitude selection and imperfection modelling method on the member capacity is considered. The study's outcomes show that the initial geometric imperfections and input parameters selected for the generated numerical models significantly affect the flexural behaviour of CFS hat-shaped beams.

Modeling of an Automatic Excavation Mechanism for a Parallelepiped Foundation - Application in Building Construction Preparation

Foundation excavation and general earthworks are activities that involve the machine operator in a series of repetitive and tedious operations, suggesting opportunities for automation through by the introduction of robotic technologies with subsequent improvements in the design and machine use, especially, in dangerous environments working. Automation of excavation operations can be realized by an automatically controlled excavator system that is able to perform autonomously a planned digging work. In this study, a modeling of an excavation mechanism has been represented, where the purpose is to automatically excavate a parallelepiped-shaped foundation. From existing excavation machinery in the industry, the manipulator arm of a backhoe was chosen to do the modeling. Starting from the basic input geometric parameters of the foundation to be excavated, the system gives as outputs results: A simulation of the various mechanism components movement, in addition to the automatic excavation trajectory of the parallelepiped foundation. Finally, from the soils properties of the western region of Algeria that they were measured experimentally, a resistance simulation of the various components of the mechanism was carried, to test the reliability of the mechanism in terms of deformation and Von Mises stress.

Implementation and validation of the FRi3D flux rope model in EUHFORIA

The “Flux Rope in 3D” (FRi3D, Isavnin, 2016), a coronal mass ejection (CME) model with global three-dimensional (3D) geometry, has been implemented in the space weather forecasting tool EUHFORIA (Pomoell and Poedts, 2018). By incorporating this advanced flux rope model in EUHFORIA, we aim to improve the modelling of CME flank encounters and, most importantly, the magnetic field predictions at Earth. After using synthetic events to showcase FRi3D’s capabilities of modelling CME flanks, we optimize the model to run robust simulations of real events and test its predictive capabilities. We perform observation-based modelling of the halo CME event that erupted on 12 July 2012. The geometrical input parameters are constrained using the forward modelling tool included in FRi3D with additional flux rope geometry flexibilities as compared to the pre-existing models. The magnetic field input parameters are derived using the differential evolution algorithm to fit FRi3D parameters to the in situ data at 1 AU. An observation-based approach to constrain the density of CMEs is adopted, in order to achieve a better estimation of mass corresponding to the FRi3D geometry. The CME is evolved in EUHFORIA’s heliospheric domain and a comparison of FRi3D’s predictive performance with the previously implemented spheromak CME in EUHFORIA is presented. For this event, FRi3D improves the modelling of the total magnetic field magnitude and Bz at Earth by ∼30% and ∼70%, respectively. Moreover, we compute the expected geoeffectiveness of the storm at Earth using an empirical Dst model and find that the FRi3D model improves the predictions of minimum Dst by ∼20% as compared to the spheromak CME model. Finally, we discuss the limitations of the current implementation of FRi3D in EUHFORIA and propose possible improvements.

On-Design Component-Level Multiple-Objective Optimization of a Small-Scale Cavity-Stabilized Combustor

Abstract This work presents an on-design component-level multiple-objective optimization of a small-scaled uncooled cavity-stabilized combustor. Optimization is performed at the maximum power condition of the engine thermodynamic cycle. The computational fluid dynamics simulations are managed by a supervised machine learning algorithm to divide a continuous and deterministic design space into nondominated Pareto frontier and dominated design points. Steady, compressible three-dimensional simulations are performed using a multiphase realizable k–ε RANS and nonadiabatic flamelet/progress variable combustion model. Conjugate heat transfer through the combustor liner is also considered. There are fifteen geometrical input parameters and four objective functions viz., maximization of combustion efficiency, and minimization of total pressure losses, pattern factor, and critical liner area factor. The baseline combustor design is based on engineering guidelines developed over the past two decades. The small-scale baseline design performs remarkably well. Direct optimization calculations are performed on this baseline design. In terms of Pareto optimality, the baseline design remains in the Pareto frontier throughout the optimization. However, the optimization calculations show improvement from an initial design point population to later iteration design points. The optimization calculations report other nondominated designs in the Pareto frontier. The Euclidean distance from design points to the Utopic point is used to select a “best” and “worst” design point for future fabrication and experimentation. The methodology to perform computational fluid dynamics optimization calculations of a small-scale uncooled combustor is expected to be useful for guiding the design and development of future gas turbine combustors.

Geometric system analysis of ILMD-based LID measurement systems using Monte-Carlo simulation

Imaging Luminance Measuring Device (ILMD) based luminous intensity distribution measurement systems are an established method for measuring the luminous intensity distribution (LID) of light sources in the far field. The advantage of this system is the high-resolution acquisition of a large angular range with one image. For the uncertainty budget, the mathematical description of the system can be divided into photometric and geometric contributions. In the following, we will present a Monte-Carlo approach to analyse the geometric contributions which are the uncertainty of measurement direction and measurement distance. Therefore, we set up a geometric system description based on kinematic transformations that describes the connection between detector and light source position. To consider all relevant input quantities we simulate the adjustment and measurement process. Finally, an analysis of the geometric input parameters is shown.

Sensitive parameter identification and uncertainty quantification for the stability of pipeline conveying fluid

Several uncertainty quantification and sensitivity analysis methods are used to determine the most sensitive geometric and material input parameters of a cantilevered pipeline conveying fluid when uncertainty is introduced to the system at the onset of instability. The full nonlinear equations of motion are modeled using the extended Hamilton’s principle and then discretized using Galerkin’s method. A parametric study is first performed, and the Morris elementary effects are calculated to obtain a preliminary understanding of how the onset speed changes when each parameter is introduced to a ± 5% uncertainty. Then, four different input uncertainty distributions, mainly, uniform and Gaussian distribution, are chosen to investigate how input distributions affect uncertainty in the output. A convergence analysis is used to determine the number of samples needed to maintain simulation accuracy while saving the most computational time. Then, Monte Carlo simulations are run, and the output distributions for each input distribution at ± 1%, ± 3% and ± 5% input uncertainty range are found and discussed. Additionally, the Pearson correlation coefficients are evaluated for different uncertainty ranges. A final Monte Carlo study is performed in which single parameters are held constant while all others still have uncertainty. Overall, the flow speed at the onset of instability is the most sensitive to changes in the outer diameter of the pipe.

Combined Damage Influence Prediction of Curved Composite Structural Responses Using VCCT Technique and Experimental Verification

The influences of crack and delamination on the natural frequency and strain energy release rate of the laminated doubly curved shell structure are computed via a commercial finite element tool (ABAQUS). The effect of individual damages (crack and delamination) is modeled using the virtual crack closure technique (VCCT), considering the curvature effect. Initially, the model validity is established by comparing the results with the available results in the open domain. Additionally, the model validity has been verified via in-house experimentation for frequency responses. Further, the natural frequency and strain energy release rate (SERR) have been calculated for the structure to examine the influences of the individual or combined effect of damages by varying the design-dependent input geometrical parameters. The inclusive characteristics of the current model in conjunction with geometrical configurations are summarized for subsequent references.

Hydraulic Design of the Center-Pivot Irrigation System for Gradually Decreasing Sprinkler Spacing

Design strategies that enhance modern irrigation practices, reduce energy consumption, and improve water use efficiency and crop yields are fundamental for sustainability. Although microirrigation is currently a widely applied method, center-pivot irrigation systems have become very popular on large farms, thanks to their automation, wide-coverage, and reliability. Different design procedures have been proposed, even though some aspects have not been solved yet. This paper presents a simple design procedure for center-pivot systems using a gradually decreasing sprinkler spacing along with a pivot lateral, which makes it possible to set favorable and uniformly distributed water application rates. The sprinkler spacing distribution along the radial direction is derived by considering just one dimensionless group accounting for the geometric and hydraulic input parameters. According to this outcome, the results showed that the suggested procedure made it possible to select the sprinkler characteristics and the pipe diameter based on the desired input parameters, i.e., the uniform water application rate and the lateral length. For assigned input parameters, the lateral length is delimited by a threshold value, indicating that lateral lengths longer than that threshold value require the modifications of a sprinkler flow rate or pipe diameter. Finally, applications based on the proposed hydraulic design procedure were performed and discussed for two different cases.

Heat and water flux modeling in an earth dam.

This study aims to identify the water flux in an earth dam using heat flux due to convection. Sixteen earth dam models were constructed in a hydraulic flume by varying geometrical and flow input parameters to identify heat and water flux. Homogeneous as well as earth dams with clay cores were built in a hydraulic flume. Temperature measurements were done to calculate heat flux in the experimental model. A finite element model of the earth dam using Seep/w was developed to obtain water flux, while temp/w was used to obtain heat flux. These results were used as input in Temp/w and Seep/w in Geostudio 2020. Significant reduction of the heat and water flux was seen while comparing the homogeneous models with central impervious core models. An increase in the heat and water flux was observed on increasing the downstream filter's length, longitudinal slope, and vice versa with the upstream slope and the thickness of the clay core. Comparing fluxes in a homogeneous dam model (model 1) with the clay core model (model 9) with top width 2.4 m and bottom width 18 m in model 9, both water flux and heat flux were reduced by 78.46%. While comparing it with model 10, with bottom core width of 18 m and top core width of 1.9 m, both water flux and heat flux reduced by 77.72%. Heat flux measurements were found to be a valuable alternative to detecting water flux and seepage in an earth dam at a reduced cost.

TFC-PREDIM: A FE dimensioning procedure for the TF coil system of a DEMO tokamak reactor

Abstract The equatorial plane of the inner leg of a toroidal field (TF) coil is the most stressed part of the TF coil system and optimal usage of the radial space in this region is crucial for the design of the DEMO tokamak reactor. A procedure for initial dimensioning (pre-dimensioning) of this region developed earlier [ 1 ] is based on a simplified 2D geometry of the TF coil cross-section and a semi-analytical approach to estimate stresses in the TF coil case and the conductor jackets with limited capabilities for optimization. This work presents a new procedure named TFC-PREDIM which features a detailed 2D finite element (FE) generalized plane strain model. The model gives very accurate results within a short calculation time and allows additional options for layout optimization. The FE model for the cross-section is generated automatically from the input geometrical parameters of the given layout allowing any of the conductor design options currently investigated for DEMO. The post-processing of the results is also automated, consisting in the determination of the maximum values of the membrane and membrane + bending Tresca stresses for each conductor jacket and for the TF coil case. Two strategies for the optimization of the cross-section are presented: minimization of the TF coil radial build with a predefined maximal toroidal field and maximization of the maximal toroidal field for a predefined radial space.

Design and evaluation of large area strip sensor prototypes for the ATLAS Inner Tracker detector

The ATLAS community is facing the last stages prior to the production of the upgraded silicon strip Inner Tracker for the High-Luminosity Large Hadron Collider. An extensive Market Survey was carried out in order to evaluate the capability of different foundries to fabricate large area silicon strip sensors, satisfying the ATLAS specifications. The semiconductor manufacturing company, Infineon Technologies AG, was one of the two foundries, along with Hamamatsu Photonics K.K., that reached the last stage of the evaluation for the production of the new devices. The full prototype wafer layout for the participation of Infineon, called ATLAS17LS-IFX, was designed using a newly developed Python-based Automatic Layout Generation Tool, able to rapidly design sensors with different characteristics and dimensions based on a few geometrical and technological input parameters. This work presents the layout design process and the results obtained from the evaluation of the new Infineon large area sensors before and after proton and neutron irradiations, up to fluences expected in the inner layers of the future ATLAS detector.